GANs usually adaptively fuse suitable text information into the synthesis process with multiple isolated fusion blocks such as Conditional Batch Normalization (CBN) and Instance Normalization (CIN). CIN \(et\ al\)  [1]} is first proposed for style transfer. Afterwards, BigGAN [2]} and Style-GAN [3]} synthesize natural images with impressing visual quality based on CBN and CIN, respectively. Recently, DF-GAN [4]}, DT-GAN [5]} and SSGAN [6]} use CIN and CBN to fuse text information into synthesized images.
Despite their popularity, CIN and CBN suffer from a serious drawback in that they are isolated in different layers, which ignores the global assignment of text information fused in different layers. Furthermore, isolated fusion blocks are hard to optimize since they do not interact with each other.
